Spatial Data and Intelligence

This book constitutes the refereed post proceedings of the 5th China Conference on Spatial Data and Intelligence, SpatialDI 2024, held in Nanjing, China, during April 25-27, 2024.

The 25 full papers included in this book were carefully reviewed and selected from 95 submissions. They were organized in topical sections as follows: Spatiotemporal Data Analysis, Spatiotemporal Data Mining, Spatiotemporal Data Prediction, Remote Sensing Data Classification and Applications of Spatiotemporal Data Mining.

.- Spatiotemporal Data Analysis.  

.- Multi-view Contrastive Clustering with Clustering Guidance and Adaptive Auto-en-coders.  

.- Cloud-Edge Collaborative Continual Adaptation for ITS Object Detection.  

.- Understanding Spatial Dependency among Spatial Interactions.  

.- An Improved DBSCAN Clustering Method for AIS Trajectories Incorporating DP Compression and Discrete Fréchet Distance.  

.- Structure and Semantic Contrastive Learning for Nodes Clustering in Heterogeneous Information Networks.  

.- Accuracy Evaluation Method for Vector Data Based on Hexagonal Discrete Global Grid.  

.- Applying Segment Anything Model to Ground-Based Video Surveillance for Identify-ing Aquatic Plant.  

.- Spatiotemporal Data Mining.  

.- Mining Regional High Utility Co-location Pattern.  

.- Local Co-location Pattern Mining Based on Regional Embedding.  

.- RCPM_RLM: A Regional Co-location Pattern Mining Method Based on Representa-tion Learning Model.  

.- Construction of a Large-Scale Maritime Elements Semantic Schema Based on Hetero-geneous Graph Models.  

.- OCGATL: One-Class Graph Attention Networks with Transformation Learning for Anomaly Detection For Argo Data.  

.- RGCNdist2vec: Using Graph Convolutional Networks and Distance2Vector to Esti-mate Shortest Path Distance along Road Networks.  

.- Self-supervised Graph Neural Network based Community Search over Heterogeneous Information Networks.  

.- Measurement and Research on the Conflict between Residential Space and Tourism Space in Pianyan Ancient Township.  

.- Spatiotemporal Data Prediction.  

.- Spatio-Temporal Sequence Prediction Of Diversion Tunnel Based On Machine Learn-ing Multivariate Data Fusion.  

.- DyAdapTransformer: Dynamic Adaptive Spatial-Temporal Graph Transformer for Traffic Prediction.  

.- Predicting Future Spatio-Temporal States Using a Robust Causal Graph Attention Model.  

.- Remote Sensing Data Classification.  

.- MADB-RemdNet for Few-Shot Learning in Remote Sensing Classification.  

.- Convolutional Neural Network Based on Multiple Attention Mechanisms for Hyper-spectral and LiDAR Classification.  

.- Few-shot Learning Remote Scene Classification Based On DC-2DEC.  

.- Applications of Spatiotemporal Data Mining.  

.- Neural HD Map Generation from Multiple Vectorized Tiles Locally Produced by Au-tonomous Vehicles.  

.- Trajectory Data Semi-fragile Watermarking Algorithm Considering Spatiotemporal Features.  

.- HPO-LGBM-DRI: Dynamic Recognition Interval Estimation for Imbalanced Fraud Call via HPO-LGBM.  

.- A Review on Urban Modelling for Future Smart Cities.